AI Recommendation Systems Quiz

Answers and explanations appear as you take the quiz.

1. What does 'collaborative filtering' rely on to make recommendations?
   • Patterns across many users with similar tastes
   • The text description of each item only
   • The price of the product
   • A fixed editorial top-10 list
2. Content-based filtering recommends items mainly by:
   • Random sampling of the catalog
   • Matching item features to your past preferences
   • Counting total global sales
   • Asking other users to vote
3. What did the Netflix Prize seek to improve by 10% over the Cinematch baseline?
   • Click-through rate for every recommended film
   • The number of films watched per user
   • Root mean squared error (RMSE) on the competition’s held-out ratings
   • A user’s average watch time on the service
4. In matrix factorization, users and items are represented as:
   • Single yes/no flags
   • Plain text reviews
   • Calendar dates
   • Vectors of hidden 'latent factors'
5. What mechanism did the Deep Interest Network paper propose for modeling a user’s different interests across candidate ads?
   • A local activation unit that forms a candidate-specific interest representation
   • A fixed user vector that is identical for every candidate
   • A rule that selects only the most popular ad
   • A graph algorithm that proves every ad is relevant
6. A 'hybrid' recommender is one that:
   • Only works on weekends
   • Combines collaborative and content-based methods
   • Uses no data at all
   • Recommends only the newest items
7. What is one limitation of collaborative filtering for a new item with no interactions?
   • It cannot use ratings
   • It requires item descriptions only
   • It may lack the interaction data needed to recommend the item
   • It always recommends every new item
8. What should a recommender evaluation measure besides click-through rate?
   • Only the model’s parameter count
   • Only training loss
   • Only the number of recommendations
   • User outcomes and harms such as diversity, exposure, or unwanted engagement